Overview

Dataset statistics

Number of variables13
Number of observations140112
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 MiB
Average record size in memory104.0 B

Variable types

Numeric10
Categorical3

Alerts

CMD has a high cardinality: 342 distinct values High cardinality
ts is highly correlated with label and 1 other fieldsHigh correlation
label is highly correlated with ts and 1 other fieldsHigh correlation
type is highly correlated with ts and 2 other fieldsHigh correlation
MINFLT is highly correlated with MAJFLTHigh correlation
MAJFLT is highly correlated with MINFLTHigh correlation
PID is highly correlated with typeHigh correlation
VSIZE is highly correlated with VGROWHigh correlation
VGROW is highly correlated with VSIZE and 1 other fieldsHigh correlation
RGROW is highly correlated with VGROWHigh correlation
MINFLT is highly skewed (γ1 = 339.0069078) Skewed
MAJFLT is highly skewed (γ1 = 135.0365829) Skewed
RGROW is highly skewed (γ1 = 25.81573545) Skewed
MINFLT has 67018 (47.8%) zeros Zeros
MAJFLT has 136555 (97.5%) zeros Zeros
VSTEXT has 7780 (5.6%) zeros Zeros
VSIZE has 7830 (5.6%) zeros Zeros
RSIZE has 7834 (5.6%) zeros Zeros
VGROW has 132559 (94.6%) zeros Zeros
RGROW has 128837 (92.0%) zeros Zeros
MEM has 109996 (78.5%) zeros Zeros

Reproduction

Analysis started2022-11-12 02:05:37.704658
Analysis finished2022-11-12 02:06:16.465667
Duration38.76 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

ts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct131032
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1554978473
Minimum1554218915
Maximum1556549129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:16.624383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1554218915
5-th percentile1554253943
Q11554394054
median1554569192
Q31556208088
95-th percentile1556306420
Maximum1556549129
Range2330214
Interquartile range (IQR)1814034.25

Descriptive statistics

Standard deviation814232.4719
Coefficient of variation (CV)0.0005236294174
Kurtosis-1.122518002
Mean1554978473
Median Absolute Deviation (MAD)200560
Skewness0.8758078916
Sum2.178711438 × 1014
Variance6.629745182 × 1011
MonotonicityNot monotonic
2022-11-12T07:36:16.800910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15562387682
 
< 0.1%
15562144682
 
< 0.1%
15562145032
 
< 0.1%
15562144932
 
< 0.1%
15562144882
 
< 0.1%
15562144832
 
< 0.1%
15562144782
 
< 0.1%
15562144732
 
< 0.1%
15562144632
 
< 0.1%
15562145132
 
< 0.1%
Other values (131022)140092
> 99.9%
ValueCountFrequency (%)
15542189151
< 0.1%
15542189201
< 0.1%
15542189251
< 0.1%
15542189301
< 0.1%
15542189351
< 0.1%
15542189401
< 0.1%
15542189451
< 0.1%
15542189501
< 0.1%
15542189551
< 0.1%
15542189601
< 0.1%
ValueCountFrequency (%)
15565491292
< 0.1%
15565486392
< 0.1%
15565483642
< 0.1%
15565479142
< 0.1%
15565474642
< 0.1%
15565473592
< 0.1%
15565473542
< 0.1%
15565473442
< 0.1%
15565472642
< 0.1%
15565472242
< 0.1%

PID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2821
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3266.943538
Minimum1007
Maximum53096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:17.303356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile1371
Q12533
median3155
Q33895
95-th percentile4801
Maximum53096
Range52089
Interquartile range (IQR)1362

Descriptive statistics

Standard deviation2119.039113
Coefficient of variation (CV)0.6486304671
Kurtosis381.9108966
Mean3266.943538
Median Absolute Deviation (MAD)638
Skewness16.49806154
Sum457737993
Variance4490326.762
MonotonicityNot monotonic
2022-11-12T07:36:17.488739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37904179
 
3.0%
37934159
 
3.0%
27743955
 
2.8%
36763954
 
2.8%
36753954
 
2.8%
14423954
 
2.8%
36773954
 
2.8%
36783954
 
2.8%
13713952
 
2.8%
27973950
 
2.8%
Other values (2811)100147
71.5%
ValueCountFrequency (%)
1007155
0.1%
1026137
0.1%
106340
 
< 0.1%
108712
 
< 0.1%
110337
 
< 0.1%
1124121
0.1%
113358
 
< 0.1%
1134205
0.1%
11351
 
< 0.1%
113733
 
< 0.1%
ValueCountFrequency (%)
530961
< 0.1%
530551
< 0.1%
530481
< 0.1%
530461
< 0.1%
530441
< 0.1%
530432
< 0.1%
530421
< 0.1%
530411
< 0.1%
530401
< 0.1%
530301
< 0.1%

MINFLT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3560
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.4218197
Minimum0
Maximum8050000
Zeros67018
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:17.648719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q31960
95-th percentile2073
Maximum8050000
Range8050000
Interquartile range (IQR)1960

Descriptive statistics

Standard deviation22333.44673
Coefficient of variation (CV)28.87615291
Kurtosis120818.217
Mean773.4218197
Median Absolute Deviation (MAD)3
Skewness339.0069078
Sum108365678
Variance498782842.9
MonotonicityNot monotonic
2022-11-12T07:36:17.898754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067018
47.8%
19639583
 
6.8%
19608600
 
6.1%
85634
 
4.0%
73909
 
2.8%
20723301
 
2.4%
33021
 
2.2%
21411
 
1.0%
20751372
 
1.0%
91294
 
0.9%
Other values (3550)34969
25.0%
ValueCountFrequency (%)
067018
47.8%
11109
 
0.8%
21411
 
1.0%
33021
 
2.2%
4818
 
0.6%
5763
 
0.5%
6722
 
0.5%
73909
 
2.8%
85634
 
4.0%
91294
 
0.9%
ValueCountFrequency (%)
80500001
< 0.1%
19000001
< 0.1%
8595021
< 0.1%
3562241
< 0.1%
2968441
< 0.1%
2473541
< 0.1%
1565271
< 0.1%
1564761
< 0.1%
1564691
< 0.1%
1564671
< 0.1%

MAJFLT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct261
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.203921863
Minimum0
Maximum107776
Zeros136555
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:18.226675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum107776
Range107776
Interquartile range (IQR)0

Descriptive statistics

Standard deviation462.0865911
Coefficient of variation (CV)74.4829805
Kurtosis24764.68521
Mean6.203921863
Median Absolute Deviation (MAD)0
Skewness135.0365829
Sum869243.9
Variance213524.0177
MonotonicityNot monotonic
2022-11-12T07:36:18.442513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0136555
97.5%
11417
 
1.0%
2429
 
0.3%
3243
 
0.2%
4196
 
0.1%
5145
 
0.1%
675
 
0.1%
868
 
< 0.1%
757
 
< 0.1%
1051
 
< 0.1%
Other values (251)876
 
0.6%
ValueCountFrequency (%)
0136555
97.5%
11417
 
1.0%
1.12
 
< 0.1%
2429
 
0.3%
2.91
 
< 0.1%
3243
 
0.2%
4196
 
0.1%
5145
 
0.1%
675
 
0.1%
757
 
< 0.1%
ValueCountFrequency (%)
1077761
< 0.1%
507761
< 0.1%
451131
< 0.1%
443921
< 0.1%
361611
< 0.1%
350011
< 0.1%
319681
< 0.1%
298802
< 0.1%
281861
< 0.1%
244721
< 0.1%

VSTEXT
Real number (ℝ≥0)

ZEROS

Distinct147
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.7817118
Minimum0
Maximum50652
Zeros7780
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:18.611627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147
median148
Q3589
95-th percentile2219
Maximum50652
Range50652
Interquartile range (IQR)542

Descriptive statistics

Standard deviation1373.721981
Coefficient of variation (CV)2.373471644
Kurtosis52.49758787
Mean578.7817118
Median Absolute Deviation (MAD)112
Skewness6.149402649
Sum81094263.2
Variance1887112.081
MonotonicityNot monotonic
2022-11-12T07:36:18.761013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14838364
27.4%
3613268
 
9.5%
07780
 
5.6%
105303
 
3.8%
21325254
 
3.7%
13505233
 
3.7%
22195197
 
3.7%
5895149
 
3.7%
5964980
 
3.6%
664944
 
3.5%
Other values (137)44640
31.9%
ValueCountFrequency (%)
07780
5.6%
358
 
< 0.1%
41142
 
0.8%
6121
 
0.1%
7125
 
0.1%
9121
 
0.1%
105303
3.8%
1152
 
< 0.1%
121932
 
1.4%
1336
 
< 0.1%
ValueCountFrequency (%)
506521
< 0.1%
282121
< 0.1%
226681
< 0.1%
216361
< 0.1%
215562
< 0.1%
194401
< 0.1%
172642
< 0.1%
166401
< 0.1%
149161
< 0.1%
136281
< 0.1%

VSIZE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1128
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8547.21741
Minimum0
Maximum78328
Zeros7830
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:18.930992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1114.7
median710.1
Q317948
95-th percentile22440
Maximum78328
Range78328
Interquartile range (IQR)17833.3

Descriptive statistics

Standard deviation11445.85971
Coefficient of variation (CV)1.339132862
Kurtosis4.195778917
Mean8547.21741
Median Absolute Deviation (MAD)708.7
Skewness1.611845947
Sum1197567726
Variance131007704.6
MonotonicityNot monotonic
2022-11-12T07:36:19.079028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1794812639
 
9.0%
1.48926
 
6.4%
07830
 
5.6%
192965239
 
3.7%
176444503
 
3.2%
2.94372
 
3.1%
114.73949
 
2.8%
2.43944
 
2.8%
176403797
 
2.7%
710.13725
 
2.7%
Other values (1118)81188
57.9%
ValueCountFrequency (%)
07830
5.6%
199
 
0.1%
1.184
 
0.1%
1.2115
 
0.1%
1.31468
 
1.0%
1.48926
6.4%
1.582
 
0.1%
1.618
 
< 0.1%
1.7268
 
0.2%
1.8414
 
0.3%
ValueCountFrequency (%)
78328117
0.1%
7686031
 
< 0.1%
7536491
0.1%
71240102
0.1%
70492130
0.1%
7030099
0.1%
686921
 
< 0.1%
640281
 
< 0.1%
6316428
 
< 0.1%
617284
 
< 0.1%

RSIZE
Real number (ℝ≥0)

ZEROS

Distinct4197
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10193.1164
Minimum0
Maximum99496
Zeros7834
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:19.278840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11588
median2604
Q311064
95-th percentile51760
Maximum99496
Range99496
Interquartile range (IQR)9476

Descriptive statistics

Standard deviation17039.08105
Coefficient of variation (CV)1.67162626
Kurtosis7.631734703
Mean10193.1164
Median Absolute Deviation (MAD)2180
Skewness2.711704469
Sum1428177925
Variance290330282.9
MonotonicityNot monotonic
2022-11-12T07:36:19.464537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07834
 
5.6%
26126681
 
4.8%
26043904
 
2.8%
26083896
 
2.8%
98.23171
 
2.3%
5682834
 
2.0%
134882825
 
2.0%
591882798
 
2.0%
82042796
 
2.0%
24842462
 
1.8%
Other values (4187)100911
72.0%
ValueCountFrequency (%)
07834
5.6%
41
 
< 0.1%
122
 
< 0.1%
162
 
< 0.1%
201
 
< 0.1%
323
 
< 0.1%
481
 
< 0.1%
521
 
< 0.1%
601
 
< 0.1%
962
 
< 0.1%
ValueCountFrequency (%)
994964
< 0.1%
992801
 
< 0.1%
992761
 
< 0.1%
992641
 
< 0.1%
991761
 
< 0.1%
990441
 
< 0.1%
990401
 
< 0.1%
990242
< 0.1%
989362
< 0.1%
989204
< 0.1%

VGROW
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct556
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean401.7808125
Minimum0
Maximum99268
Zeros132559
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:19.630108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum99268
Range99268
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4378.613411
Coefficient of variation (CV)10.89801522
Kurtosis178.2719434
Mean401.7808125
Median Absolute Deviation (MAD)0
Skewness12.83247441
Sum56294313.2
Variance19172255.4
MonotonicityNot monotonic
2022-11-12T07:36:19.772629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0132559
94.6%
41397
 
1.0%
132637
 
0.5%
256628
 
0.4%
128590
 
0.4%
40474
 
0.3%
29840353
 
0.3%
51980240
 
0.2%
8188
 
0.1%
1024168
 
0.1%
Other values (546)2878
 
2.1%
ValueCountFrequency (%)
0132559
94.6%
0.214
 
< 0.1%
0.42
 
< 0.1%
12
 
< 0.1%
1.32
 
< 0.1%
1.42
 
< 0.1%
1.52
 
< 0.1%
1.71
 
< 0.1%
21
 
< 0.1%
2.31
 
< 0.1%
ValueCountFrequency (%)
992682
< 0.1%
901881
 
< 0.1%
886721
 
< 0.1%
836681
 
< 0.1%
817402
< 0.1%
785681
 
< 0.1%
783284
< 0.1%
774841
 
< 0.1%
768602
< 0.1%
765521
 
< 0.1%

RGROW
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1782
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.9474956
Minimum0
Maximum98920
Zeros128837
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:19.920158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile68
Maximum98920
Range98920
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1543.19942
Coefficient of variation (CV)10.79556808
Kurtosis1070.48339
Mean142.9474956
Median Absolute Deviation (MAD)0
Skewness25.81573545
Sum20028659.5
Variance2381464.451
MonotonicityNot monotonic
2022-11-12T07:36:20.102331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0128837
92.0%
41053
 
0.8%
8599
 
0.4%
12383
 
0.3%
20326
 
0.2%
952306
 
0.2%
16296
 
0.2%
24216
 
0.2%
260178
 
0.1%
28151
 
0.1%
Other values (1772)7767
 
5.5%
ValueCountFrequency (%)
0128837
92.0%
0.12
 
< 0.1%
0.41
 
< 0.1%
41053
 
0.8%
8599
 
0.4%
9.82
 
< 0.1%
104
 
< 0.1%
10.16
 
< 0.1%
10.29
 
< 0.1%
10.38
 
< 0.1%
ValueCountFrequency (%)
989201
< 0.1%
987161
< 0.1%
971801
< 0.1%
944001
< 0.1%
928641
< 0.1%
886801
< 0.1%
875481
< 0.1%
770521
< 0.1%
681401
< 0.1%
617841
< 0.1%

MEM
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004468782117
Minimum0
Maximum0.16
Zeros109996
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-12T07:36:20.231942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.03
Maximum0.16
Range0.16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01158754176
Coefficient of variation (CV)2.592997701
Kurtosis23.00815332
Mean0.004468782117
Median Absolute Deviation (MAD)0
Skewness4.124365351
Sum626.13
Variance0.000134271124
MonotonicityNot monotonic
2022-11-12T07:36:20.332194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0109996
78.5%
0.0115796
 
11.3%
0.025999
 
4.3%
0.035389
 
3.8%
0.06864
 
0.6%
0.05646
 
0.5%
0.07597
 
0.4%
0.04302
 
0.2%
0.08222
 
0.2%
0.09141
 
0.1%
Other values (5)160
 
0.1%
ValueCountFrequency (%)
0109996
78.5%
0.0115796
 
11.3%
0.025999
 
4.3%
0.035389
 
3.8%
0.04302
 
0.2%
0.05646
 
0.5%
0.06864
 
0.6%
0.07597
 
0.4%
0.08222
 
0.2%
0.09141
 
0.1%
ValueCountFrequency (%)
0.161
 
< 0.1%
0.153
 
< 0.1%
0.1436
 
< 0.1%
0.1141
 
< 0.1%
0.179
 
0.1%
0.09141
 
0.1%
0.08222
 
0.2%
0.07597
0.4%
0.06864
0.6%
0.05646
0.5%

CMD
Categorical

HIGH CARDINALITY

Distinct342
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
atop
37995 
vmtoolsd
8028 
compiz
 
5279
ostinato
 
5254
irqbalance
 
5241
Other values (337)
78315 

Length

Max length19
Median length13
Mean length7.78562864
Min length2

Characters and Unicode

Total characters1090860
Distinct characters52
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowXorg
2nd rowhaveged
3rd rowkworker/0:1
4th row<kworker/u256>
5th rowcompiz

Common Values

ValueCountFrequency (%)
atop37995
27.1%
vmtoolsd8028
 
5.7%
compiz5279
 
3.8%
ostinato5254
 
3.7%
irqbalance5241
 
3.7%
nautilus5234
 
3.7%
Xorg5197
 
3.7%
hud-service5149
 
3.7%
apache25004
 
3.6%
drone4943
 
3.5%
Other values (332)52788
37.7%

Length

2022-11-12T07:36:20.465856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atop38004
27.1%
vmtoolsd8028
 
5.7%
compiz5289
 
3.8%
ostinato5254
 
3.7%
irqbalance5241
 
3.7%
nautilus5241
 
3.7%
xorg5197
 
3.7%
hud-service5149
 
3.7%
apache25116
 
3.7%
drone4943
 
3.5%
Other values (286)52668
37.6%

Most occurring characters

ValueCountFrequency (%)
o113488
 
10.4%
t102172
 
9.4%
a101358
 
9.3%
e77887
 
7.1%
p75237
 
6.9%
n58440
 
5.4%
s57318
 
5.3%
i52241
 
4.8%
d49978
 
4.6%
u45992
 
4.2%
Other values (42)356749
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter998649
91.5%
Dash Punctuation45210
 
4.1%
Decimal Number22559
 
2.1%
Other Punctuation9267
 
0.8%
Uppercase Letter9051
 
0.8%
Math Symbol6029
 
0.6%
Connector Punctuation75
 
< 0.1%
Space Separator18
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o113488
11.4%
t102172
 
10.2%
a101358
 
10.1%
e77887
 
7.8%
p75237
 
7.5%
n58440
 
5.9%
s57318
 
5.7%
i52241
 
5.2%
d49978
 
5.0%
u45992
 
4.6%
Other values (16)264538
26.5%
Decimal Number
ValueCountFrequency (%)
210981
48.7%
53819
 
16.9%
63772
 
16.7%
11669
 
7.4%
01661
 
7.4%
3650
 
2.9%
43
 
< 0.1%
82
 
< 0.1%
72
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
X5197
57.4%
C1278
 
14.1%
W1258
 
13.9%
M699
 
7.7%
N617
 
6.8%
T2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/4662
50.3%
:4584
49.5%
.21
 
0.2%
Math Symbol
ValueCountFrequency (%)
<3017
50.0%
>3010
49.9%
~2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-45210
100.0%
Connector Punctuation
ValueCountFrequency (%)
_75
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1007700
92.4%
Common83160
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o113488
11.3%
t102172
 
10.1%
a101358
 
10.1%
e77887
 
7.7%
p75237
 
7.5%
n58440
 
5.8%
s57318
 
5.7%
i52241
 
5.2%
d49978
 
5.0%
u45992
 
4.6%
Other values (22)273589
27.1%
Common
ValueCountFrequency (%)
-45210
54.4%
210981
 
13.2%
/4662
 
5.6%
:4584
 
5.5%
53819
 
4.6%
63772
 
4.5%
<3017
 
3.6%
>3010
 
3.6%
11669
 
2.0%
01661
 
2.0%
Other values (10)775
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1090860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o113488
 
10.4%
t102172
 
9.4%
a101358
 
9.3%
e77887
 
7.1%
p75237
 
6.9%
n58440
 
5.4%
s57318
 
5.3%
i52241
 
4.8%
d49978
 
4.6%
u45992
 
4.2%
Other values (42)356749
32.7%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
100000 
1
40112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters140112
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0100000
71.4%
140112
28.6%

Length

2022-11-12T07:36:20.604023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T07:36:20.728637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0100000
71.4%
140112
28.6%

Most occurring characters

ValueCountFrequency (%)
0100000
71.4%
140112
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number140112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100000
71.4%
140112
28.6%

Most occurring scripts

ValueCountFrequency (%)
Common140112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0100000
71.4%
140112
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII140112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0100000
71.4%
140112
28.6%

type
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
normal
100000 
dos
 
10000
ddos
 
10000
injection
 
10000
password
 
10000

Length

Max length9
Median length6
Mean length5.998401279
Min length3

Characters and Unicode

Total characters840448
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdos
2nd rowdos
3rd rowdos
4th rowdos
5th rowdos

Common Values

ValueCountFrequency (%)
normal100000
71.4%
dos10000
 
7.1%
ddos10000
 
7.1%
injection10000
 
7.1%
password10000
 
7.1%
mitm112
 
0.1%

Length

2022-11-12T07:36:20.827733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T07:36:20.976544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal100000
71.4%
dos10000
 
7.1%
ddos10000
 
7.1%
injection10000
 
7.1%
password10000
 
7.1%
mitm112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o140000
16.7%
n120000
14.3%
r110000
13.1%
a110000
13.1%
m100224
11.9%
l100000
11.9%
d40000
 
4.8%
s40000
 
4.8%
i20112
 
2.4%
t10112
 
1.2%
Other values (5)50000
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter840448
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o140000
16.7%
n120000
14.3%
r110000
13.1%
a110000
13.1%
m100224
11.9%
l100000
11.9%
d40000
 
4.8%
s40000
 
4.8%
i20112
 
2.4%
t10112
 
1.2%
Other values (5)50000
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin840448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o140000
16.7%
n120000
14.3%
r110000
13.1%
a110000
13.1%
m100224
11.9%
l100000
11.9%
d40000
 
4.8%
s40000
 
4.8%
i20112
 
2.4%
t10112
 
1.2%
Other values (5)50000
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII840448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o140000
16.7%
n120000
14.3%
r110000
13.1%
a110000
13.1%
m100224
11.9%
l100000
11.9%
d40000
 
4.8%
s40000
 
4.8%
i20112
 
2.4%
t10112
 
1.2%
Other values (5)50000
 
5.9%

Interactions

2022-11-12T07:36:12.913888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:51.893851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:54.487074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:56.986964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:59.753755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:02.114384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:04.733772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:06.897911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:09.140682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:11.099221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:13.240072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:52.336970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:54.659870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:57.170826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:59.923885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:02.285714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:04.963992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:07.055176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:09.328332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:11.290314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:13.664293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:52.581471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:54.923176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:57.335037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:00.145997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:02.481160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:05.192595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:07.211721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:09.525495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:11.454106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:13.881248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:52.772973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:55.103672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:57.506869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:00.496908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:02.740272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:05.458097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:07.404353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:09.705870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:11.617772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:14.120134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:53.028960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:55.314536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:57.912190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:00.715446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:03.177834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:05.670090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:07.693192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:09.942374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:11.780583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:14.383047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:53.305801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:55.581451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:58.176053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:00.906713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:03.497801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:05.839656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:08.184063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:10.110340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:11.962175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:14.639089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:53.519278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:55.807469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:58.467019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:01.187773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:03.802267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:06.028011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:08.425978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:10.292685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:12.190232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:14.819550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:53.755057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:56.074974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:58.820089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:01.473745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:04.169980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:06.209736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:08.594824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:10.526070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:12.342292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:15.017559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:53.951408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:56.417185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:59.075001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:01.774910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:04.376957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:06.388859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:08.773252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:10.689451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:12.509210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:15.208110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:54.238923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:56.621473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:35:59.405643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:01.938530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:04.548982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:06.661986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:08.950129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:10.852494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T07:36:12.706369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-12T07:36:21.123795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-12T07:36:21.277970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-12T07:36:21.505552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-12T07:36:21.720290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-12T07:36:21.868105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-12T07:36:21.973867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-12T07:36:15.600080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-12T07:36:16.024243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

tsPIDMINFLTMAJFLTVSTEXTVSIZERSIZEVGROWRGROWMEMCMDlabeltype
01556129658149400.02219.0390.082020.00.00.00.02Xorg1dos
11556129738164100.012.09480.03496.00.00.00.00haveged1dos
21556129778660400.00.00.00.00.00.00.00kworker/0:11dos
315561297885101700.00.00.00.00.00.00.00<kworker/u256>1dos
41556129798276600.010.01.368724.00.00.00.02compiz1dos
51556129823314400.02132.02.525800.00.00.00.01ostinato1dos
615561298981424100.036.019296.0692.00.00.00.00irqbalance1dos
715561299135277950.00.00.00.00.00.00.00<systemd-host>1dos
81556129923276600.010.01.368724.00.00.00.02compiz1dos
91556129933147300.010415.0607.734644.00.00.00.01mysqld1dos

Last rows

tsPIDMINFLTMAJFLTVSTEXTVSIZERSIZEVGROWRGROWMEMCMDlabeltype
1401021554256745367520760.0148.017948.02604.00.00.00.00atop0normal
1401031554256750367620760.0148.018016.02580.00.00.00.00atop0normal
1401041554256755437520770.0148.017952.02464.00.00.00.00atop0normal
1401051554256760437420770.0148.017948.02456.00.00.00.00atop0normal
1401061554256765437220760.0148.017644.02412.00.00.00.00atop0normal
1401071554256770437320760.0148.017648.02408.00.00.00.00atop0normal
1401081554256775185100.036.0159.82000.00.00.00.00vmtoolsd0normal
1401091554256780137150.036.019296.0640.00.00.00.00irqbalance0normal
1401101554256785166800.012.09480.0292.00.00.00.00haveged0normal
14011115542567901442190222.02219.0811.2255.711764.015736.00.07Xorg0normal